The present invention relates to a real-time, high-resolution cerebral biopotential analysis system and method, and more particularly to a computer-based biopotential diagnostic system and method for quantitatively determining, in a noninvasive manner, cerebral phenomena that can be ascertained by analyzing the properties of cerebral electrical activity.
Despite a considerable expenditure of time and effort, current approaches to the quantitative, noninvasive assessment of cerebral electrical activity, as displayed in an electroencephalographic (EEG) waveform, have not been successful in fully extracting all of the information which is present in this complex waveform. A great need remains for an accurate, sensitive, reliable, and practical neurological profiling technology. In particular, contemporary intraoperative EEG monitoring techniques have not been widely adopted due to their inherent limitations. Indeed, large numbers of medical malpractice suits are believed to be related to post-anesthesia morbidity and mortality, and if such EEG monitoring techniques were reliable they certainly would have been adopted.
A number of devices known in the prior art are capable of tracking cerebral activity qualitatively. Techniques involving the use of the classical conventional analog EEG are restricted to analyses in the time domain, and require considerable training for adequate interpretation. Moreover, since the resolution of the human eye at standard EEG tracing speeds is limited, much of the fine structure of the EEG is invisible. Thus, visual EEG assessment is better characterized as an art rather than a science.
The use of frequency (power spectrum) analysis of the EEG in the 1960s introduced the notion of some basic processing of the signal prior to visual inspection and led to the application of frequency analysis of the EEG to various cerebral monitoring problems. In the past 25 years, over 100 papers have been published in the medical literature describing applications of power spectral analysis for purposes such as assessing the depth of anesthesia and cerebral ischemia under various intraoperative conditions. U.S. Pat. No. 4,557,270 issued to John also describes the use of power spectrum analysis to evaluate cerebral perfusion during open heart surgery. Several recent studies, however, have shown many deficiencies in the use of power spectral analysis to monitor cerebral perfusion and to determine postoperative neurological outcome. In addition, neither power spectrum analysis nor any other monitoring technique has been shown to be reliable, demonstrated by the fact that the Harvard Medical School Anesthesia Monitoring Standard does not include any type of intraoperative neurological monitoring, due, in all likelihood, to the complexity of interpreting raw EEG data and the unreliability of existing automated systems utilizing power spectrum or time-domain analytic techniques.
The discharge of thousands of bioelectrically active cells in the brain, organized in larger, interacting neural centers contributes to the formation of an electrical signal with a wide frequency spectrum that is rich in harmonics and extremely complex dynamics. Embedded in that signal is information regarding frequency content, nonlinearities, and phase relationships arising from the complex neuronal firing patterns that take place. Such firing patterns change constantly making the statistical properties of the EEG signal highly nonstationary. Because of the complexity of the EEG signal, conventional time and frequency modes of analysis have not been able to fully profile its behavior. This may be one of the reasons for the limited success of such approaches.
In the Fourier transform of the second order autocorrelation function (the power spectrum), processes are represented as a linear summation of statistically-uncorrelated sine-shaped wave components. Contemporary approaches to monitoring the EEG by means of the power spectrum have thus suppressed information regarding nonlinearities and inter-frequency phase relationships and are of limited utility in representing the EEG's dynamic structure.
Because the EEG is highly dynamic and nonlinear, the phase relationships within the EEG are the elements most likely to carry diagnostic information regarding cerebral function. The Fourier transform of the third order autocorrelation function, or autobispectrum, is an analytic process that quantifies deviation from normality, quadratic nonlinearities and inter-frequency phase relationships within a signal. The Fourier transform of the third order cross correlation function, or cross bispectrum, is an analytic process that provides similar information for two signals. We can generalize these techniques by defining the Fourier transform of the nth-order auto/cross correlation function, or the n-1 order auto/cross spectrum, as an analytic process that contains information regarding deviation from normality, as well as n-1 order nonlinearities and inter-frequency phase relationships in a signal. Auto/cross spectra beyond the bispectrum will be referred to as higher-order spectra.
Autobispectrum analysis techniques have been applied to the EEG signal to demonstrate the basic bispectral properties of the conventional EEG. Such studies have also been conducted to search for differences between the waking and sleeping states. Autobispectrum analysis and power spectrum analysis have also been used in an attempt to show that the EEGs of monozygotic twins are similar in structure. U.S. Pat. Nos. 4,907,597 and 5,010,891 issued to Chamoun describe the use of auto/cross bispectrum analysis of the EEG to evaluate cerebral phenomena such as quantifying depth and adequacy of anesthesia, pain responses induced by surgical stress, cerebral ischemia, consciousness, degrees of intoxication, ongoing cognitive processes and inter-hemispheric dynamic phase relations.
To date, no one has used auto higher-order spectrum or cross higher-order spectrum analysis for neurological diagnoses or monitoring of the cerebral phenomena described above.
A common problem in analyzing the data generated by any of the spectral techniques discussed above is the fact that the EEG's frequency distribution may dramatically change under relatively stable physiological conditions. Such changes will lead to changes in the power spectrum, bispectrum, and higher order spectra at the corresponding frequencies. For example, when hypnotic anesthetic agents are administered in low to medium concentrations, there is a substantial increase in the EEG activity in the 12-18 Hz frequency band. High doses of the same agents will lead to a sudden reduction in activity in the 12-18 Hz band and increase in activity in the 0.5-3.5 Hz band, followed by burst suppression at extremely high concentrations. A frequency-based analysis that uses the 12-18 Hz frequency band to track the patient's anesthetic depth during the administration of a hypnotic agent will provide a misleading assessment of the patient's depth when the shift in activity from high to low frequency occurs. Such transitions are even more complicated when a mixture of anesthetic agents is used.
Therefore, a principal object of the present invention is to provide a noninvasive high resolution electroencephalographic system and method capable of recognizing and monitoring physical phenomena that are reflected in properties of cerebral electrical activity.
Another object of the present invention is to provide a noninvasive electroencephalographic system and method capable of determining and monitoring depth and adequacy of anesthesia, pain responses during surgical stress, cerebral ischemia, cerebral hypoxia, levels of consciousness, degrees of intoxication, altered evoked potential responses, and normal or abnormal cognitive processes including but not limited to identifying patients with Alzheimer's disease and HIV-related dementias.